Flood Magnitude Research Articles

Aggregating flood damage functions: The peril of Jensen's gap

AbstractFlood risk models provide important information for disaster planning through estimating flood damage to exposed assets, such as houses. At large scales, computational constraints or data coarseness often lead modelers to aggregate asset data using a single statistic (e.g., the mean) prior to applying non‐linear damage functions. This practice of aggregating inputs to nonlinear functions introduces error and is known as Jensen's inequality; however, the impact of this practice on flood risk models has so far not been investigated. With a Germany‐wide approach, we isolate and compute the error resulting from aggregating four typical concave damage functions under 12 scenarios for flood magnitude and aggregation size. In line with Jensen's 1906 proof, all scenarios result in an overestimate, with the most extreme scenario of a 1 km aggregation for the 500‐year flood risk map yielding a country‐wide average bias of 1.19. Further, we show this bias varies across regions, with one region yielding a bias of 1.58 for this scenario. This work applies Jensen's 1906 proof in a new context to demonstrate that all flood damage models with concave functions will introduce a positive bias when aggregating and that this bias can be significant.

Flash flood dynamics in the foothills of the NW Himalayas: insights into hydrological and morphological controls.

The Himalayas experiences several cloudburst events due to its varied physiographical, geomorphological, and geological conditions and high rainfall. Uttarakhand is one of the Indian states circumscribed by the Himalayan ranges and has experienced a rise in the number of cloudburst catastrophes in the last few decades. These events cause substantial loss of life and property; however, very few studies have characterized these unpredictable cloudburst-induced flash floods in different regions of Uttarakhand. This study examines the geological and hydrological factors associated with the Raipur-Kumalda cloudburst event that occurred from 20 to 21 August 2022 in the Dehradun district of Uttarakhand. The resulting flash flood caused significant damage to roads, bridges, and settlements across the valley. The study aims to understand the geological and geomorphological controls of the event by analyzing the peak discharge and various flood parameters. The basin geomorphometry and rainfall intensity of the region reveal poorly developed drainage networks with low drainage density, steep slopes, rapid peak flows, a sharp peak hydrograph, and intense, concentrated rainfall, all of which worsen the impact of the flood. Various flood indices, including the rising curve gradient (K), flood magnitude ratio (M), and flood response time (TP), indicate a discharge 50 to 100 times higher during the event compared to the average monsoonal discharge. This study also discusses the role of mountain topography, climate, regional geology, and irreversible land use-land cover (LULC) changes associated with urbanization in intensifying the destruction.

The influence of tropical cyclones on mixed-population flood frequency analysis

Abstract Floods induced by tropical cyclones (TCs) and their frequency analysis have received much attention at the regional scale, but little is known about the role of TCs in mixed flood frequency analysis at the global scale, which is the key for hazard management. Here, we investigated the influence of TCs on the upper tail of flood frequency estimates using discharge observations at 3,883 stations influenced by TCs alongside global discharge simulations from the ISIMIP 2a. Our results show that TC-induced floods in inland areas are not always found along the TC path but can be located about 1,000 km ahead, which is linked to TC-induced predecessor rain events. Extreme floods associated with TCs are generally larger in coastal areas compared to those in inland areas, whereas TC-induced floods in some inland areas tend to be more extreme relative to their local flood magnitude. The flood peaks generated by TCs, and other mechanisms compose a mixed population flood series. However, the largest flood peaks in coastal areas are usually affected by TCs. These large TC-induced flood peaks play an important role in making the upper tail of the flood frequency curve heavier. In regions affected by TC, the flood magnitude with n-year return period is greater when estimated from TC-induced floods only. Therefore, regions (especially inland regions) should thoroughly consider the extremity of TC-induced fooding when improving hydraulic infrastructures to ensure a more conservative design, thereby enhancing the safety of these hydraulic projects.

Climatic Extremes Drive Abrupt Shifts in the Distribution of Franklin’s Gull

Franklin’s Gull (Leucophaeus pipixcan) nests in marshes, primarily in the Great Plains, Great Basin, and Intermountain West. On the periphery of the range, its nesting was first documented in Lower Klamath National Wildlife Refuge in 1989, but numbers remained very low through the early 1990s. After first spiking in the Klamath Basin in 2003, numbers generally were modest through 2013, then increased greatly (many hundreds to low thousands) in most years from 2014 to 2022, but dropped sharply in 2023. In 2012, observers counted 100 nests at Lower Klamath, and other indirect evidence implied nesting at or near Lower Klamath and Tule Lake national wildlife refuges from 1994 to 2022. These changes are part of a broader pattern of increases in adjacent regions. Colonization of the Klamath Basin followed displacement of gulls from flooded marshes at Great Salt Lake and elsewhere in the mid-1980s, but from the early 1980s onward likely was facilitated by local management that greatly extended early-successional marshes. Since 2003, spikes in Franklin’s Gull numbers in the Klamath Basin have largely coincided with extended drought in areas with the nearest large colonies in Utah, Idaho, and southeastern Oregon. Rangewide population trends are poorly documented and difficult to track because the breeding marshes’ dynamism drives variation in colony sizes and locations. As climate warming increases the magnitude and frequency of droughts and floods, more accurate monitoring of population trends in Franklin’s Gull is needed. Meanwhile, anecdotal observations may give early warnings of population fluctuations and range shifts.

Evolution of flood protection levels and flood vulnerability in Europe since 1950 estimated with vine-copula models

AbstractThe magnitude of flood impacts is regulated not only by hydrometeorological hazard and exposure, but also flood protection levels (primarily from structural flood defenses) and vulnerability (relative loss at given intensity of hazard). Here, we infer the variation of protection levels and vulnerability from data on historical riverine, coastal, and compound floods and associated impacts obtained from the HANZE database, in 42 European countries over the period 1950–2020. We contrast actual damaging floods, which imply flood protection was locally inadequate, with modelled potential floods, i.e. events that were hydrologically extreme but did not lead to significant impacts, which imply that flood protection was sufficient to prevent losses. Further, we compare the reported magnitude of impacts (fatalities, population affected, and economic losses) with potential impacts computed with depth-damage functions. We finally derive the spatial and temporal drivers of both flood protection and vulnerability through a multivariate statistical analysis. We apply vine-copulas to derive the best predictors out of a set of candidate variables, including hydrological parameters of floods, exposure to floods, socioeconomic development, and governance indicators. Our results show that riverine flood protection levels are much lower than assumed in previous pan-European studies. North-western Europe is shown to have better riverine protection than the south and east, while the divide is not so clear for coastal protection. By contrast, many parts of western Europe have relatively high vulnerability, with lowest value observed in central and northern Europe. Still, a strong decline in flood vulnerability over time is also observed for all three indicators of relative losses, suggesting improved flood adaptation. Flood protection levels have also improved since 1950, particularly for coastal floods.

Shifting cold regions streamflow regimes in North America affect flood frequency analysis

ABSTRACT Over-threshold flood events in 70 years of data from 202 reference hydrometric stations in Canada and the United States were separated into nival, mixed, and pluvial flood types. While Mann-Kendall trend tests showed few significant trends in flood magnitude, significant changes in flood type fraction were found over time, with annual mean temperature, and with annual precipitation. Nival events decreased in frequency over the 70-year period in 16% of sites, while mixed and pluvial events increased (5%, 12%). These changes indicate a shift from nival events towards more pluvial-dominated systems. Fewer significant changes in flood type fraction were found with analysis against four climate indices. Flood frequency analysis using a combined distribution approach with the three flood types resulted in larger magnitude design flow estimates (median increase of 20–30%) in comparison with the results from considering the data as a single population.

Integrating the cellular vortex method with remote sensing and geographical information systems in the modelling of coastal flooding around Niger Delta

Coastal areas are increasingly vulnerable to flooding, necessitating accurate simulation methods to understand flood dynamics and their potential impacts. This study employed a Lagrangian framework integrating the cellular vortex method with remote sensing and GIS to simulate flood height distribution in a coastal region. Leveraging climatic and remotely sensed data, alongside ArcMap 10.6.1 for map processing, the research estimated flood magnitude and frequency using the L-moment approach, applied to a forty-year tidal record dataset. Essential input parameters, such as the roughness coefficient and curve number, were derived from land use and land cover characteristics. Additionally, river flow velocity was observed at 0.12m/s, with measured wind speed and direction recorded at 4m/s in the northwest direction. Notably, analysis of the initial flood height distribution map revealed a significant expansion of wetland areas, attributed to observed land use changes between May 2002 and July 2005. Projections for flood height distribution in 2025 and 2050 highlighted the emergence of tidal floods, emphasizing the critical role of considering future climate and land use scenarios in flood dynamics assessment. This research contributes to advancing understanding of flood modeling techniques and underscores the urgency of adaptive measures to mitigate the potential impacts of coastal flooding.

Research on flood warning model based on dimensionality reduction and integrated neural network

Flood disasters have long-term impacts on human societies and ecosystems, and population growth and urbanization lead to changes in surface conditions that increase the frequency and magnitude of floods. Therefore, understanding and predicting flood probability and constructing a flood warning mechanism are urgent problems. In this paper, a flood warning mechanism based on the probability of flood occurrence is proposed to construct a high-performance and highly interpretable model in a step-by-step manner using machine learning and deep learning methods. First, the correlation level is elucidated by the PCA dimensionality reduction method. Secondly, the K-means clustering method is used to replace the qcut binning method, and the classification model is built by combining the multidimensional data point distribution and classified by polynomial logistic regression. Finally, the integrated neural network model is constructed, the visual model performance is used for full feature optimization, and the performance of the classification model is optimized to achieve the flood prediction effect through the mean square error as the combination of the weights of the integrated model.

MODELING FLOOD SUSCEPTIBILITY IN HYDROLOGICAL DATA-SCARCE STUDY AREAS

Hydrological information is essential for estimating the magnitude of floods, thus helping reduce losses and damage. However, when this data is scarce, it challenges flood modeling and risk assessment. In Brazil's west of Rio Grande do Sul, we used the Santa Maria hydrographic basin as a case study to estimate the river flow for different RP based on the statistical distribution of rainfall and river flow data and to simulate flood scenarios using a 2D IBER hydrological model.River flow estimates for 5, 10, 20, 30, and 50-year RP were obtained using the Giandotti equation. The results showed a high correlation between the river flows calculated from rainfall and the fluviometric data, using GEV and GumbelMax distribution. The areas susceptible to flooding are consistent with past flood events, validated with fieldwork and Sentinel imagery. River flows estimated from rainfall data can promote hydrological studies where fluviometric data is absent. Keywords: River Flow Estimation; Generalized Extreme Values; Flood Susceptibility; Rosário Do Sul; Brazil.

Strengthening of the hydrological cycle in the Lake Chad Basin under current climate change

Central Sahel is affected by a reinforcement of rainfall since the beginning of 1990s. This increase in rainfall is affected by high inter-annual variability and is characterized by extreme rain events causing floods of unprecedented magnitude. However, few studies have been carried out on these extreme events. Moreover, with current climate change expected to strengthen the hydrological cycle, we don’t know if these events could become more frequent. Here, we report the hydrological changes that currently occur in the Lake Chad basin. Based on ground observations and satellite data, we focused on the 2022 flood event, demonstrating that it was the most important event from the last 60 years, comparable to what occurred during the last wet period between the 1950s and the 1960s. We showed that under this precipitation regime and if warming is not regulated at a global scale, the return period of the 2022 major riverine flood is expected to be between 2 and 5 years. By using modelling experiments, our study also suggested that in the next decade, future flow rates of the main rivers draining the Lake Chad basin could reach the values observed in the 1950s. These results strongly suggest anticipating water management in a context of poor infrastructural development.

Flood frequency analysis in the lower Burhi Dehing River in Assam, India using Gumbel Extreme Value and log Pearson Type III methods

Flood is a widespread climate-related hazard with the potential to occur in almost any geographical location where fluvial processes are active and can occur due to the involvement of multiple factors. Flood frequency analysis (FFA) is considered a valuable hydrologic tool to know the magnitude and frequency of the recurrence of floods. In this research, we have examined the Lower Burhi Dehing River (LBDR) in Assam, India, which frequently experiences severe flooding due to its meandering course induced by heavy monsoon rainfall. Therefore, the present research aims to analyze past flood events and current trends and predict future flood frequencies using Gumbel’s extreme value distribution and Log Pearson Type III Method. In this particular investigation, 25 years of annual maximum peak discharge data from 1972 to 1997 was employed to examine the discharge records of LBDR. This research estimated the return periods of different flood magnitudes, quantifying the likelihood of future floods with varying degrees of severity. In this regard, the return periods are estimated at 2, 5, 10, 20, 50, 100, and 200 years. The study reveals that the largest flood event occurred in 1972, with a discharge of 1134.4 m3/s, and the smallest flood event occurred in 1997, with a discharge of 214.65 m3/s. The 2-year flood is a relatively common event with a 50% probability of occurring in any given year, characterized by a moderate discharge of 1384.90 m3/s and minor impacts of 0.3665. The 5-year flood, occurring with a 20% chance annually, brings a significantly higher water level of 2008.81 m3/s and moderate-to-severe impacts of 1.4999, potentially causing widespread flooding. In the 10-year and 50-year return period, which has a probability of returning 10% and 2% chance annually, the discharge will reach 2421.89 m3/s and 3331.01 m3/s, respectively. The outcome of this research will sustainably guide policymakers and environmental planners to mitigate flood hazards.

Spatial Variation of Changes in Extreme Discharge Seasonality Across the Northeastern United States

ABSTRACTThe Northeast United States exhibits significant spatial heterogeneity in flood seasonality, with spring snowmelt‐driven floods historically dominating northern areas, while other regions show more varied flood seasonality. While it is well documented that since 1996 there has been a marked increase in extreme precipitation across this region, the response of flood seasonality to these changes in extreme precipitation and the spatial distribution of these effects remain uncertain. Here we show that, historically, snowmelt‐dominated northern regions were relatively insensitive to changes in extreme precipitation. However, with climate warming, the dominance of snowmelt floods is decreasing and thus the extreme flood regimes in northern regions are increasingly susceptible to changes in extreme precipitation. While extreme precipitation increased everywhere in the Northeastern United States in 1996, it has since returned to near pre‐1996 levels in the coastal north while remaining elevated in the inland north. Thus, the inland north region has and continues to experience the greatest changes in extreme flooding seasonality, including a substantial rise in floods outside the historical spring flood season, particularly in smaller watersheds. Further analysis reveals that while early winter floods are increasingly common, the magnitude of cold season floods (Nov‐May) have remained unchanged over time. In contrast, warm season floods (June‐Oct), historically less significant, are now increasing in both frequency and magnitude in the inland north. Our results highlight that treating the entire Northeast as a uniform hydroclimatic region conceals significant regional variations in extreme discharge trends and, more generally, climate warming will likely increase the sensitivity of historically snowmelt dominated watersheds to extreme precipitation. Understanding this spatial variability in increased extreme precipitation and increased sensitivity to extreme precipitation is crucial for enhancing disaster preparedness and refining water management strategies in affected regions.

Frequency and Spatial Variability of European Record Floods

AbstractRegional envelope curves (RECs) have been used to characterize the flooding potential of regions worldwide. However, a comprehensive assessment for Europe is still missing. In this study we use the largest European flood database to quantify the magnitude of record floods, their dependence on catchment size, and the amount of available independent information, which is needed for estimatinig the REC's exceedance probability. We propose a framework for estimating REC parameters across large areas, consisting in partitioning the domain according to the observation density and estimating the REC slope through quantile regression. The results show that the REC parameters vary substantially across Europe. The envelope unit flood associated with a catchment area of 1,000 km2 varies between 0.1 and 6 m3s−1 km−2 and is highest in Mediterranean and Alpine areas and lowest in central‐eastern Europe. The slope of the envelope varies between 0 and −1 and is larger in Southern Europe than in Northern Europe. These differences are explained by steeper landscape in mountaineous regions and localized short duration storms producing large floods in small catchments in the Mediterranean, whose intensities are larger than in other parts of Europe. Based on the framework of probabilistic envelope curves we show that, despite the uneven observation density, the obtained RECs are associated with comparable exceedance probabilities.

Flood simulation and impact analysis of Xun River under backwater condition

Abstract The intersection of river channels is easy to cause the backside of the main flow, which greatly influences the change of hydrological conditions of river channels. To study the changing characteristics of river flood under the condition of uplift, a one-dimensional hydrodynamic model was established to simulate the evolution process of river flood under the condition of seeking river uplift. Factors such as distance, height, flood speed, inundation area, inundation depth, and flood speed were selected to analyze the changing rules of the influence of river bullying. The results show that the influence degree of the river following is closely related to the size of the flood. Under the same earthquake magnitude, the backwater distance, backwater height, inundation area, inundation depth, and inundation speed of sub-river flood increase with the increase of flood magnitude, while the flood speed decreases. When the magnitude of the flood is once in 200 years, the flood elements reach the maximum, the reflux distance is 8, 368 m, the reflux height is 3.2 m, and the velocity is reduced by 43.2%. This study can provide relevant data for the flood control work in the Sun River basin and provide a reference for the scheme design of tributary interchanges.

Experimental Investigation of Bridge Scour under Pressure Flow Conditions

Recent studies have revealed that the frequency and magnitude of floods tend to increase due to climate change. Hence, excessive scouring due to flood events puts river bridges at greater risk of failure. This paper presents the initial findings of an experimental study to improve the understanding of the main characteristics of bridge pier scour under pressurized flow encountered during flooding. The experiments were carried out in four main groups according to two deck alignments with circular and oblong pier shapes. For each group of experiments, thirty-six tests were conducted under partially and fully pressurized flow conditions using four approach flow depths and three discharge values. The validity of the structured design approach for pier scour estimation implemented in the guidelines was investigated. The results showed that the bridge pier scour depths were up to 29.4% and 49.4% greater than the sum of the vertical contraction and local scour depths for 100 L/s for partially and fully pressurized flow conditions, respectively. However, as the discharge increased to 120 L/s, the bridge pier scour depth became 38.3% and 17.8% smaller than the sum of the vertical contraction and local scour depths for partially and fully pressurized flow, respectively. So, the structured design approach was determined to be safe for high discharge values. Furthermore, it was found that tests with a circular pier resulted in higher bridge pier scour depths than the sum of the vertical contraction and local scour depths up to 19.3% even for 120 L/s. Conversely, smaller bridge pier scour depths than the sum of the vertical contraction and local scour depths were observed up to 17.8% for tests with oblong piers. Thus, it can be concluded that the pier shape has a profound effect on scour holes and oblong piers cause smaller scour depths than circular piers in pressurized flow conditions. This study showed that the flow–pier–deck interaction significantly affects the depth and width of the scour hole, especially for small discharges and fully pressurized flow conditions.

Evaluation of probability distribution methods for flood frequency analysis in the Jhelum Basin of North-Western Himalayas, India

The Kashmir Valley has frequently endured devastating floods, presenting significant challenges for flood management due to unpredictable flood magnitudes and limited pre-disaster preparedness. A major difficulty in addressing these challenges is the lack of reliable flood frequency analysis (FFA) for effective planning and mitigation. This study seeks to overcome these issues by employing a rigorous quantitative analysis of annual peak discharge data over a 51-year period (1971–2021). One key challenge was the presence of low outliers, which could compromise the integrity of the data. To address this, the Multiple Grubbs-Beck test was applied to remove these outliers before conducting FFA. The study's original achievement lies in its application of multiple distribution models which include Gumbel (EV1), Generalized Extreme Variations (GEV), Log-Normal, Log Pearson III (LP III), Gamma and Normal distribution. Goodness-of-fit tests, including Kolmogorov-Smirnov (KS), Anderson-Darling (AD), and Chi-square at the 5 % significance level, along with visualization techniques such as Probability plots (PP), Quantile plots (QQ), and Probabilistic distribution (PD) graphs, were used to identify the most suitable distribution methods. The Log Pearson Type III (LP-III) was identified as the best fit for the Sangam gauge site (Upper Jhelum), the gamma distribution for Ram Munshibagh (Middle Jhelum), and the Generalized Extreme Value (GEV) and LP-III for Asham (Lower Jhelum). For Sangam, the estimated discharges for 2, 5, 10, 50, 100, 150, 200, and 250-year return periods were 549.63, 1028.43, 1471.34, 2907.64, 3758.92, 4338.61, 4790.99, and 5167.23 cumecs, respectively, using LP-III. For Ram Munshibagh, the discharges were 602.13, 911.03, 1107.04, 1512.12, 1674.35, 1767.0, 1831.87, and 1881.74 cumecs using the gamma distribution. For Asham, the discharges were 685.8, 998.0, 1193.3, 1593.2, 1750.6, 1839.4, 1901.0, and 1948.0 cumecs using the GEV distribution. The findings indicate that the Jhelum River cannot accommodate excess discharge for return periods of 5 years or more, underscoring the need for enhanced flood management strategies.

Numerical Modeling of Extreme Sea Levels on the Laptev Sea Coast

The present study is devoted to the analysis of extreme sea level oscillations of the Laptev Sea using the ADCIRC model. The numerical modeling is performed on a high-resolution grid and verified for sea level observations from three tide gauges. We have revealed regional characteristics of extreme sea level oscillations for different parts of the Laptev Sea coast. The maximum total sea level range was 544 cm in Ebelyakh Bay, while the minimum was 267 cm in Khatanga Bay, where maximum tidal ranges were obtained. Some areas in Khatanga Bay and Anabar Bay had maximum tidal ranges exceeding 200 cm. The study provided an estimation of the possible magnitude of coastal flooding by calculating the extreme total and residual sea levels for different return periods: 1, 2, 5, 10, 20, 50, and 100 years. The amplitude of extreme surges calculated for the 100-year return period can exceed 300 cm for several sections of the Laptev Sea coast, with the maximum sea level range being about 680 cm for Anabar and Ebelyakh Bays.

Nivar cyclone-related urban flood vulnerability in Velachery municipality, Chennai

ABSTRACT The Velachery Municipality is highly vulnerable to natural disasters such as Storms, and floods due to its geo-environmental settings. It is evident that the Nivar cyclone has severely hit and caused flooding in the Velachery region during the fourth week of November 2020. Along with the increasing urbanization, it is also expected to experience severe impacts of climate change soon. Urbanization exerts additional environmental stress and amplifies the impact of natural disasters. The study area has been divided into 3 clusters based on the geographical location. The survey has been carried out in 180 households and data was statically analyzed. Indicators such as exposure, sensitivity, and adaptive capacity were grouped and aligned with available data. Data were aggregated using a composite index and Urban Flood Vulnerability was compared to Cluster wise. The results reveal an extreme variability in terms of flood magnitude and frequency in Velachery. The vulnerability spider diagram suggests that Cluster 2 is more vulnerable in terms of less capacity to cope, and Cluster 3 is more vulnerable in terms of Exposure, Health, Economic susceptibility, physical susceptibility, less capacity to anticipate, cope and recover. Overall Cluster 3 has higher flood vulnerability compared to other clusters. The identification of flood-vulnerable zones has been done in the study and recommended flood resilience measures for Velachery Municipality, Chennai.

Investigating the influence of urban morphology on pluvial flooding: Insights from urban catchments in England (UK)

As climate change intensifies, cities globally are experiencing more severe rainfall and frequent pluvial floods. Urban expansion is altering the permeability of the land, thus increasing the risk of flooding. This study investigates the impact of urban morphology on pluvial floodwater distribution in 15 urban catchments across England, UK, to provide an analysis of how urban morphology influences flood magnitude. Using a cellular automata-based model, pluvial flood simulations were conducted for catchments characterized by diverse urban morphologies. Then a series of machine learning models were adopted to reveal the relationships between the morphological characteristics of urban configurations (e.g., building footprints, impervious surfaces, street network, topography) and pluvial flooding. These models were used to identify and quantify the effects of key urban morphological indicators on pluvial flooding. The results indicate that, although the total area of impervious surfaces plays the most significant role in floodwater distribution, the edge density (ED) of building footprints and impervious surfaces also influences this process. Synthetic experiments with an exemplary urban fabric show that decreasing “ED of building footprint” and increasing “ED of impervious surface” can mitigate flood volume by up to 6.3 % at 100 % drainage efficiency and 7.8 % at 50 % efficiency. The results of this study are anticipated to aid urban planners and policymakers in developing strategies for implementing flood-resilient cities.

Change and coincidence risk analysis of floods in the Mahanadi River Basin, India

ABSTRACT Coincidence flood risk due to the simultaneous flood occurrences on both mainstream and its tributary results in downstream inundation of a confluence. Therefore, this study was taken up for coincidence flood risk analysis of Mahanadi River basin considering the annual maximum (AM) and the peak over threshold (POT) series. In this study, the Mann–Kendall trend test was performed to analyze the trend in flood magnitudes, while circular statistics was used to analyze the persistence in flood timing. The joint distributions between the streams were established using bivariate copula functions considering flood magnitudes and occurrence dates as variables. The results of MK test revealed a mixture of significant and insignificant trends for AM series for the selected stations, while the trends were insignificant for POT series. Additionally, the flood occurrence dates showed a high level of persistence. It is evident from the results that the coincidence risk is high for Seorinarayan–Bamnidhi confluence point with a risk value of 7.63 × 10−3. The coincidence risk increases mostly from late July to mid-September, and the coincidence risk is high for more frequently occurring flood events. The obtained results will help in prioritizing flood hazard zones for effective flood mitigation strategies in Mahanadi River basin.